translation: Update replace_linear_by_hashing.md (#1551)

* translation: Update replace_linear_by_hashing.md

refined some parts of it.

* Update replace_linear_by_hashing.md

* Update replace_linear_by_hashing.md

---------

Co-authored-by: Yudong Jin <krahets@163.com>
This commit is contained in:
K3v123 2024-12-04 23:05:03 +13:00 committed by GitHub
parent abf1f115bf
commit b6939da46c
No known key found for this signature in database
GPG key ID: B5690EEEBB952194

View file

@ -1,6 +1,6 @@
# Hash optimization strategies
In algorithm problems, **we often reduce the time complexity of algorithms by replacing linear search with hash search**. Let's use an algorithm problem to deepen understanding.
In algorithm problems, **we often reduce the time complexity of an algorithm by replacing a linear search with a hash-based search**. Let's use an algorithm problem to deepen the understanding.
!!! question
@ -8,7 +8,7 @@ In algorithm problems, **we often reduce the time complexity of algorithms by re
## Linear search: trading time for space
Consider traversing all possible combinations directly. As shown in the figure below, we initiate a two-layer loop, and in each round, we determine whether the sum of the two integers equals `target`. If so, we return their indices.
Consider traversing through all possible combinations directly. As shown in the figure below, we initiate a nested loop, and in each iteration, we determine whether the sum of the two integers equals `target`. If so, we return their indices.
![Linear search solution for two-sum problem](replace_linear_by_hashing.assets/two_sum_brute_force.png)
@ -18,11 +18,11 @@ The code is shown below:
[file]{two_sum}-[class]{}-[func]{two_sum_brute_force}
```
This method has a time complexity of $O(n^2)$ and a space complexity of $O(1)$, which is very time-consuming with large data volumes.
This method has a time complexity of $O(n^2)$ and a space complexity of $O(1)$, which can be very time-consuming with large data volumes.
## Hash search: trading space for time
Consider using a hash table, with key-value pairs being the array elements and their indices, respectively. Loop through the array, performing the steps shown in the figure below each round.
Consider using a hash table, where the key-value pairs are the array elements and their indices, respectively. Loop through the array, performing the steps shown in the figure below during each iteration.
1. Check if the number `target - nums[i]` is in the hash table. If so, directly return the indices of these two elements.
2. Add the key-value pair `nums[i]` and index `i` to the hash table.
@ -42,6 +42,6 @@ The implementation code is shown below, requiring only a single loop:
[file]{two_sum}-[class]{}-[func]{two_sum_hash_table}
```
This method reduces the time complexity from $O(n^2)$ to $O(n)$ by using hash search, greatly improving the running efficiency.
This method reduces the time complexity from $O(n^2)$ to $O(n)$ by using hash search, significantly enhancing runtime efficiency.
As it requires maintaining an additional hash table, the space complexity is $O(n)$. **Nevertheless, this method has a more balanced time-space efficiency overall, making it the optimal solution for this problem**.